Reliability analysis of Birnbaum–Saunders model based on progressive type-II censoring

References

• Birnbaum ZW, Saunders SC. A new family of life distribution. J Appl Probab. 1969(a);6:319–327. doi: 10.2307/3212003
   Web of Science ®Google Scholar
• Birnbaum ZW, Saunders SC. Estimation for a family of life distributions with applications to fatigue. J Appl Probab. 1969(b);6:328–347. doi: 10.2307/3212004
   Web of Science ®Google Scholar
• Leiva V, Ruggeri F, Saulo H, et al. A methodology based on the Birnbaum-Saunders distribution for reliability an analysis applied to nano-materials. Reliab Eng Syst Saf. 2017;157:192–201. doi: 10.1016/j.ress.2016.08.024
   Web of Science ®Google Scholar
• Leiva V, Athayde E, Azevedo C, et al. Modeling wind energy flux by a Birnbaum-Saunders distribution with an unknown shift parameter. J Appl Stat. 2011;38(12):2819–2838. doi: 10.1080/02664763.2011.570319
   Web of Science ®Google Scholar
• Leiva V, Sanhueza A, Angulo JM. A length-biased version of the Birnbaum-Saunders distribution with application in water quality. Stoch Environ Res Risk Assess. 2009;23:299–307. doi: 10.1007/s00477-008-0215-9
   Web of Science ®Google Scholar
• Owen WJ, Padgett WJ. A Birnbaum-Saunders accelerated life model. IEEE Trans Reliab. 2000;49(1):224–229. doi: 10.1109/24.877342
   Web of Science ®Google Scholar
• Villegas C, Paula G, Leiva V. Birnbaum-Saunders mixed models for censored reliability data analysis. IEEE Trans Reliab. 2011;60:748–758. doi: 10.1109/TR.2011.2170251
   Web of Science ®Google Scholar
• Leiva V, Rojas E, Galea M, et al. Diagnostics in Birnbaum-Saunders accelerated life models with an application to fatigue data. Appl Stoch Models Bus Ind. 2014;30(2):115–131. doi: 10.1002/asmb.1944
   Web of Science ®Google Scholar
• Zhang CF, Shi YM, Bai XC, et al. Inference for constant-stress accelerated life tests with dependent competing risks from bivariate Birnbaum-Saunders distribution based on adaptive progressively hybrid censoring. IEEE Trans Reliab. 2017;66(1):111–122. doi: 10.1109/TR.2016.2639583
   Web of Science ®Google Scholar
• Soliman AA, Abd-Ellah AH, Abou-Elhggag NA, et al. Reliability estimation in stress-strength models: an MCMC approach. Statistics. 2013;47(4):715–728. doi: 10.1080/02331888.2011.637629
   Web of Science ®Google Scholar
• Mokhlis NA, Ibrahim EJ, Gharieb DM. Stress-strength reliability with general form distributions. Commun Stat Theory Methods. 2017;46(3):1230–1246. doi: 10.1080/03610926.2015.1014110
   Web of Science ®Google Scholar
• Nadar M, Kιzιlaslan F, Papadopoulos A. Classical and Bayesian estimation of P(Y<X) for Kumaraswamy's distribution. J Stat Comput Simul. 2014;84(7):1505–1529. doi: 10.1080/00949655.2012.750658
   Web of Science ®Google Scholar
• Balakrishnan N, Aggarwala R. Progressive censoring: theory, methods, and application. Boston: Birkhäuser; 2000.
   Google Scholar
• Balakrishnan N. Progressive censoring methodology: an appraisal. Test. 2007;16:211–259. doi: 10.1007/s11749-007-0061-y
   Web of Science ®Google Scholar
• Saraçoğlua B, Kinaci I, Kundu D. On estimation of R=P(Y<X) for exponential distribution under progressive type-II censoring. J Stat Comput Simul. 2012;82(5):729–744. doi: 10.1080/00949655.2010.551772
   Web of Science ®Google Scholar
• Lio YL, Tsai TR. Estimation of δ=P(X<Y) for Burr XII distribution based on the progressively first failure-censored samples. J Appl Stat. 2011;39(2):309–322. doi: 10.1080/02664763.2011.586684
   Web of Science ®Google Scholar
• Asgharzadeh A, Valiollahi R, Raqab MZ. Stress-strength reliability of Weibull distribution based on progressively censored samples. Sort-Stat Oper Res Trans. 2011;35(2):103–124.
   Web of Science ®Google Scholar
• Valiollahi R, Asgharzadeh A, Raqab MZ. Estimation of P(Y<X) for Weibull distribution under progressive Type-II censoring. Commun Stat Theory Methods. 2013;42:4476–4498. doi: 10.1080/03610926.2011.650265
   Web of Science ®Google Scholar
• Basirat M, Baratpour S, Ahmadi J. Statistical inferences for stress strength in the proportional hazard models based on progressive Type-II censored samples. J Stat Comput Simul. 2015;85(3):431–449. doi: 10.1080/00949655.2013.824449
   Web of Science ®Google Scholar
• Shoaee S, Khorram E. Stress-strength reliability of a two-parameter bathtub-shaped lifetime distribution based on progressively censored samples. Commun Stat Theory Methods. 2015;44:5306–5328. doi: 10.1080/03610926.2013.821485
   Web of Science ®Google Scholar
• Chen ZM. A new two-parameter lifetime distribution with bathtub shape or increasing failure rate function. Stat Probab Lett. 2000;49:155–161. doi: 10.1016/S0167-7152(00)00044-4
   Web of Science ®Google Scholar
• Pakyari R, Balakrishnan N. A general purpose approximate goodness-of-fit test for progressively type-II censored data. IEEE Trans Reliab. 2012;61(1):238–244. doi: 10.1109/TR.2012.2182811
   Web of Science ®Google Scholar
• Balakrishnan N, Ng HKT, Kannan N. Goodness-of-fit tests based on spacings for progressively type-II censored data from a general location-scale distribution. IEEE Trans Reliab. 2004;53(3):349–356. doi: 10.1109/TR.2004.833317
   Web of Science ®Google Scholar
• Ng HKT, Kundu D, Balakrishnan N. Point and interval estimation for the two-parameter Birnbaum Saunders distribution based on type-II censored samples. Comput Stat Data Anal. 2006;50:3222–3242. doi: 10.1016/j.csda.2005.06.002
   Web of Science ®Google Scholar
• Cysneiros AHMA, Cribari-Neto F, Araújo CAG Jr. On Birnbaum-Saunders inference. Comput Stat Data Anal. 2008;52:4938–4950. doi: 10.1016/j.csda.2008.04.003
   Web of Science ®Google Scholar
• Balakrishnan N, Zhu XJ. On the existence and uniqueness of the maximum likelihood estimates of the parameters of Birnbaum-Saunders distribution based on type-I, type-II and hybrid censored samples. Statistics. 2004;48(5):1013–1032. doi: 10.1080/02331888.2013.800069
   Web of Science ®Google Scholar
• Pradhan B, Kundu D. Inference and optimal censoring schemes for progressively censored Birnbaum-Saunders distribution. J Stat Plan Inference. 2013;143:1098–1108. doi: 10.1016/j.jspi.2012.11.007
   Web of Science ®Google Scholar
• Achcar JA. Inference for the Birnbaum-Saunders fatigue life model using Bayesian methods. Comput Stat Data Anal. 1993;15:367–380. doi: 10.1016/0167-9473(93)90170-X
   Web of Science ®Google Scholar
• Yalan Li, Ancha Xu. Fiducial inference for Birnbaum-Saunders distribution. J Stat Comput Simul. 2016;86(9):1673–1685. doi: 10.1080/00949655.2015.1077840
   Web of Science ®Google Scholar
• Barros M, Leiva V, Ospina R, et al. Goodness-of-fit tests for the Birnbaum-Saunders distribution with censored reliability data. IEEE Trans Reliab. 2014;62(2):543–554. doi: 10.1109/TR.2014.2313707
   Web of Science ®Google Scholar
• Lemonte AJ, Ferrari ALP. Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples. Comput Stat Data Anal. 2011;55:2388–2399. doi: 10.1016/j.csda.2011.02.005
   Web of Science ®Google Scholar
• Upadhyay SK, Peshwani M. Full posterior analysis of three parameter log normal distribution using gibbs sampler. J Stat Comput Simul. 2001;71(3):215–230. doi: 10.1080/00949650108812144
   Web of Science ®Google Scholar
• Upadhyay SK, Vasishta N, Smith AFM. Bayes inference in life testing and reliability via Markov Chain Monte Carlo simulation. Sankhy. 2001;63(1):15–40.
   Google Scholar
• Badar MG, Priest AM. Statistical aspects of fiber and bundle strength in hybrid composites. In: Hayashi T, Kawata K, Umekawa S, et al., editors. Progress in science and engineering composites. Tokyo: ICCM-IV; 1982. p. 1129–1136.
   Google Scholar
• Raqab MZ, Kundu D. Comparison of different estimators of P(Y<X) for a scaled Burr type X distribution. Commun Stat Simul Comput. 2005;34(2):465–483. doi: 10.1081/SAC-200055741
   Web of Science ®Google Scholar
• Kundu D, Gupta RD. Estimation of P(Y<X) for Weibull distributions. IEEE Trans Reliab. 2005;55(2):270–280. doi: 10.1109/TR.2006.874918
   Web of Science ®Google Scholar